CN103063167A - Method for judging laser cleaning effect automatically - Google Patents
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- CN103063167A CN103063167A CN2012105829286A CN201210582928A CN103063167A CN 103063167 A CN103063167 A CN 103063167A CN 2012105829286 A CN2012105829286 A CN 2012105829286A CN 201210582928 A CN201210582928 A CN 201210582928A CN 103063167 A CN103063167 A CN 103063167A
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- 238000004140 cleaning Methods 0.000 title claims abstract description 45
- 238000000034 method Methods 0.000 title claims abstract description 20
- 230000000694 effects Effects 0.000 title claims abstract description 16
- 239000007769 metal material Substances 0.000 claims description 8
- 230000003760 hair shine Effects 0.000 claims description 2
- 239000002184 metal Substances 0.000 abstract description 9
- 229910052751 metal Inorganic materials 0.000 abstract description 9
- JEIPFZHSYJVQDO-UHFFFAOYSA-N iron(III) oxide Inorganic materials O=[Fe]O[Fe]=O JEIPFZHSYJVQDO-UHFFFAOYSA-N 0.000 abstract 2
- 239000003086 colorant Substances 0.000 description 6
- 238000010586 diagram Methods 0.000 description 6
- 239000000463 material Substances 0.000 description 6
- 238000005260 corrosion Methods 0.000 description 5
- 230000007797 corrosion Effects 0.000 description 5
- 230000003287 optical effect Effects 0.000 description 5
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 description 4
- 238000013459 approach Methods 0.000 description 3
- 238000001444 catalytic combustion detection Methods 0.000 description 3
- 101100327165 Arabidopsis thaliana CCD8 gene Proteins 0.000 description 2
- 229910000831 Steel Inorganic materials 0.000 description 2
- 239000011538 cleaning material Substances 0.000 description 2
- 229910052742 iron Inorganic materials 0.000 description 2
- 239000000047 product Substances 0.000 description 2
- 230000001105 regulatory effect Effects 0.000 description 2
- 239000010959 steel Substances 0.000 description 2
- 230000000007 visual effect Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000004040 coloring Methods 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 239000012467 final product Substances 0.000 description 1
- 238000002310 reflectometry Methods 0.000 description 1
- LENZDBCJOHFCAS-UHFFFAOYSA-N tris Chemical compound OCC(N)(CO)CO LENZDBCJOHFCAS-UHFFFAOYSA-N 0.000 description 1
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Abstract
The invention discloses a method for judging laser cleaning effect automatically. The method includes that a red green blue (RGB) average value of a color image on a metal surface with rust removed is compared with an RGB average value of a color image on a metal surface without being rusted so as to judge cleaning effect, and the cleaning efficiency is further judged through a comparison between an average value of a grey-scale map on a metal surface with rust removed and an average value of a grey-scale map on a metal surface without being rusted. The method achieves the automatic judging for the laser cleaning effect.
Description
Technical field
The invention belongs to the laser cleaning field, relate in particular to a kind of automatic decision laser cleaning effect method.
Technical background
In the laser cleaning process, because the sample extent of corrosion is different, may have etch pit, so single cleans and has not same effect, the sample surfaces some places clean up, and some places also have corrosion layer.Cleaning performance can judge with cleaning efficiency, and cleaning efficiency is defined as the area that cleaned up divided by total cleaning area.Judge that at present the laser cleaning effect mainly adopts visual method to estimate and reads.But it is high that a large advantage of laser cleaning is automaticity, is convenient to be connected with subsequent handling, therefore is necessary to study a kind of method of automatic decision cleaning performance.
Summary of the invention
The purpose of this invention is to provide a kind of method that adopts the image invention to carry out the judgement of laser cleaning effect, the method will be converted to the calculating of laser cleaning area the statistics of image pixel number, utilize corrosion layer and material surface to become the different realization with brightness range of the average automatic decision of image lower of white light.
Technical scheme of the present invention is: a kind of method of automatic decision laser cleaning effect may further comprise the steps:
1) shines unattacked metal material surface with white light source, obtain the basic coloured image of described unattacked metal material surface, calculate the RGB average of basic coloured image
Described basic coloured image is converted to basic gray level image, calculates the gray threshold of described basic gray level image
2) with the sample surfaces after the white light source irradiation cleaning, obtain the coloured image of sample surfaces, the coloured image that obtains is carried out pre-service, calculate the RGB average of described coloured image
3) the RGB average of the RGB average of comparison step 2 described coloured images and the described basic coloured image of step 1), when
In time, is judged as totally and cleans up.
4) with step 2) described coloured image is converted to gray level image, with the gray scale of each pixel of gray level image
With gray threshold
Compare, when
In time, is judged as this pixel and cleans up.
5) ratio of the pixel count n that cleans up described in the described step 4) of calculating and the total pixel number N of gray level image is cleaning efficiency
Gray threshold in the described step 1)
Average gray value for described basic gray level image.
Beneficial effect of the present invention: utilize reflected light image color and the monochrome information of different material under the white light to have different characteristics, to be converted to the calculating of area pixel calculates, finish the purpose of automatic decision cleaning performance, help the robotization of laser cleaning equipment, also be convenient to integrate with other equipment, save manpower and time, and computing method are simple, computing velocity is fast, accuracy is high.
Description of drawings
Fig. 1 method flow diagram;
Fig. 2 device synoptic diagram;
Fig. 3 50W laser cleaning exterior view;
1 laser instrument; 2 optical systems; 3 controllers; 4 clean laser; 5 cleaning areas; 6 cleaning materials; 7 computing machines; 8 colored area array CCDs; 9 white light sources.
Embodiment
The method of automatic decision laser cleaning effect mainly utilizes material different, under the white light source irradiation, the different methods that detect with illuminance information of catoptrical color, whether failed, employing is calculated area and is converted to gray level image pixels statistics judgement cleaning efficiency if adopting the judgement of three primary colours average value processing to clean.
A kind of device of automatic decision laser cleaning effect such as Fig. 2, comprise laser instrument 1, optical system 2, controller 3, computing machine 7, colour plane battle array CCD8 and white light source 9, described laser instrument 1 and optical system 2 are by controller 3 controls, and optical system 2 converges to the cleaning area 5 that is cleaned material 6 surfaces with laser instrument 1 Output of laser 4 collimations.The same cleaning area 5 of white light source 9 irradiations, reflected light is received by colour plane battle array CCD8, and computing machine 7 calculates its cleaning performance.
A kind of method flow diagram of automatic decision laser cleaning effect such as Fig. 1.Before carrying out automatic decision, need to obtain first the three primary colours average of unattacked metal material surface image
With the gray level image threshold value
Because what the metal material surface that cleans up presented is the metal true qualities, and brightness is even, therefore adopts white light source irradiation metal material clean surface, and colored area array CCD is taken surface image, carry out the RGB(RGB) the three primary colours analysis, computed image RGB average threshold value
, this threshold value is close to the rgb value of metal color.Again coloured image is converted to gray level image, the gray-scale value of all pixels is added and, again divided by pixel count, and get final product
, this threshold value also is close to a constant.
Utilize the sample area after the white light source irradiation is cleaned after cleaning is finished, colored area array CCD is taken the surface image after cleaning, the image that the method pre-service of adopting contrast to strengthen gathers.Carry out equally the RGB(RGB) the three primary colours analysis, computed image RGB average
The first situation, when metal material covers corrosion layer, this moment the RGB average
Close to the rgb value of corrosion layer color, because rough surface, the light major part is scattered, and presents the shadow region, at this moment the RGB average
Might close to
The second situation, after material was cleaned totally, what present was the gloss of metal self, and owing to reflectivity improves greatly, brightness also improves much than before cleaning, so RGB average at this time
Approach or be a bit larger tham the rgb value of metal color; The third situation, when overclean, surfacing generation qualitative change, what material presented no longer is the metal intrinsic colour also, should be the color of its product, RGB average at this time
Rgb value close to the product color that is to say, can judge whether to clean up by colouring information.So with RGB average and average threshold ratio, when
The time, explanation cleans up, otherwise expression is cleaned unsuccessfully.The coloured image that cleans up is converted to gray level image, and the total element of pixel is N, represents the number of pixels that cleans up with n.Each pixel with gray level image
One by one with gray threshold
Compare, when
, explanation cleans up, and the n value adds 1, final cleaning efficiency
When cleaning efficiency reaches a certain numerical value, meet cleaning requirement.
Embodiment
At first with the clean hull steel iron surface of white light source irradiation, obtain surface image, the contrast of regulating image is 100%, tries to achieve image RGB three primary colours average threshold value [248,245,249] and gray threshold 232.
Adopt the 100W fiber pulse laser of 1064nm, pulse repetition rate is 100kHz, Duplication is 50%, and the collimation focusing optical system directly adopts the condenser lens of scanning galvanometer and 254nm, uses 10W, 50W, the laser energy of 100W cleans respectively the hull steel iron surface, and visual result is that the laser action of 10W does not clean up, and the laser of 50W cleans up substantially, the laser of 100W excessively cleans, and Fig. 3 is the exterior view of 50W laser cleaning material.After gathering image respectively, the contrast of at first regulating image is 100%, and store image information is tried to achieve respectively image RGB three primary colours average, and the image average that calculates rusty stain is [123,45,20], illustrates that the rusty stain color is for red; The laser action image of 10W is [38,35,30], and key diagram looks like not clean up near black; The laser action image of 50W is [207,214,204], and the key diagram picture approaches white, substantially cleans up; The laser action image of 100W is [103,113,91], and the key diagram picture approaches yellow, excessively cleans.Relatively compare greater than scope with average threshold value [248,245,249], only have the laser action image of 50W to meet the requirements, directly be converted to gray level image, luminance threshold is 232, compares one by one, cleaning up number is 45527, and total pixel is 61740, so cleaning efficiency is 72.74%.
Claims (3)
1. the method for an automatic decision laser cleaning effect is characterized in that, may further comprise the steps:
1) shines unattacked metal material surface with white light source, obtain the basic coloured image of described unattacked metal material surface, calculate the RGB average of basic coloured image
Described basic coloured image is converted to basic gray level image, calculates the gray threshold of described basic gray level image
2) with the sample surfaces after the white light source irradiation cleaning, obtain the coloured image of sample surfaces, the coloured image that obtains is carried out pre-service, calculate the RGB average of described coloured image
3) the RGB average of the RGB average of comparison step 2 described coloured images and the described basic coloured image of step 1), when
In time, is judged as totally and cleans up.
2. the method for a kind of automatic decision laser cleaning effect according to claim 1 is characterized in that, also comprises after the described step 3):
4) with step 2) described coloured image is converted to gray level image, with the gray scale of each pixel of gray level image
With gray threshold
Compare, when
In time, is judged as this pixel and cleans up;
5) ratio of the pixel count n that cleans up described in the described step 4) of calculating and the total pixel number N of gray level image is cleaning efficiency
3. the method for a kind of automatic decision laser cleaning effect according to claim 1 is characterized in that, the gray threshold in the described step 1)
Average gray value for described basic gray level image.
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Cited By (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104550134A (en) * | 2014-12-16 | 2015-04-29 | 江苏大学 | Device and method for removing rust corrosion of inner cavity of plastic rolling mould |
| CN105588836A (en) * | 2016-01-25 | 2016-05-18 | 江苏大学 | Device and method for detecting laser cleaning effect |
| CN105717135A (en) * | 2015-11-11 | 2016-06-29 | 南开大学 | Hyperspectral imaging-based method for monitoring laser cleaning process |
| CN105784597A (en) * | 2016-05-26 | 2016-07-20 | 秦皇岛港股份有限公司 | Protecting device for visual inspection system |
| CN106459848A (en) * | 2014-03-26 | 2017-02-22 | 索豪瑟有限公司 | Method for cleaning systems |
| CN106482060A (en) * | 2015-05-20 | 2017-03-08 | 古德里奇照明系统有限责任公司 | Airplane external illuminating device |
| CN106624367A (en) * | 2017-03-11 | 2017-05-10 | 四川广正科技有限公司 | Pretreatment method and system for material surface before laser welding |
| CN107025651A (en) * | 2017-04-25 | 2017-08-08 | 苏州德威尔卡光电技术有限公司 | The determination method and device of laser cleaning energy |
| CN107121398A (en) * | 2017-04-25 | 2017-09-01 | 苏州德威尔卡光电技术有限公司 | Determination method and device, laser cleaning method and the system of laser cleaning energy |
| CN107340302A (en) * | 2017-07-06 | 2017-11-10 | 武汉翔明激光科技有限公司 | A kind of cleaning quality monitoring device and method based on laser cleaner |
| CN107610125A (en) * | 2017-10-16 | 2018-01-19 | 云南电网有限责任公司临沧供电局 | A kind of long distance laser derusting monitoring in real time and feedback method, apparatus and system |
| CN108416771A (en) * | 2018-03-07 | 2018-08-17 | 南京工业大学 | Metal material corrosion area detection method based on monocular camera |
| CN110398499A (en) * | 2018-04-23 | 2019-11-01 | 南开大学 | A method of the monitoring laser cleaning process based on high light spectrum image-forming |
| CN111112253A (en) * | 2020-01-16 | 2020-05-08 | 福建省燕京惠泉啤酒股份有限公司 | Fermentation tank cleaning method |
| CN112718710A (en) * | 2020-12-30 | 2021-04-30 | 南开大学 | Method for analyzing copper substrate pollutants and laser cleaning effect thereof based on red, green and blue numerical values |
| CN113083804A (en) * | 2021-04-25 | 2021-07-09 | 中国铁建重工集团股份有限公司 | Laser intelligent derusting method and system and readable medium |
| CN113962994A (en) * | 2021-12-21 | 2022-01-21 | 武汉智能兴运铁路配件有限公司 | Method for detecting cleanliness of lock pin on three-connecting-rod based on image processing |
| CN114119535A (en) * | 2021-11-24 | 2022-03-01 | 上海航翼高新技术发展研究院有限公司 | Laser cleaning effect on-line monitoring method based on visual detection |
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| CN106459848B (en) * | 2014-03-26 | 2020-04-17 | 索豪瑟有限公司 | Method for cleaning a system |
| CN104550134A (en) * | 2014-12-16 | 2015-04-29 | 江苏大学 | Device and method for removing rust corrosion of inner cavity of plastic rolling mould |
| CN106482060A (en) * | 2015-05-20 | 2017-03-08 | 古德里奇照明系统有限责任公司 | Airplane external illuminating device |
| CN106482060B (en) * | 2015-05-20 | 2021-01-05 | 古德里奇照明系统有限责任公司 | Aircraft exterior lighting device |
| CN105717135A (en) * | 2015-11-11 | 2016-06-29 | 南开大学 | Hyperspectral imaging-based method for monitoring laser cleaning process |
| CN105588836B (en) * | 2016-01-25 | 2018-04-17 | 江苏大学 | A kind of device and method for detecting laser cleaning effect |
| CN105588836A (en) * | 2016-01-25 | 2016-05-18 | 江苏大学 | Device and method for detecting laser cleaning effect |
| CN105784597A (en) * | 2016-05-26 | 2016-07-20 | 秦皇岛港股份有限公司 | Protecting device for visual inspection system |
| CN105784597B (en) * | 2016-05-26 | 2018-08-10 | 秦皇岛港股份有限公司 | A kind of protective device of vision detection system |
| CN106624367A (en) * | 2017-03-11 | 2017-05-10 | 四川广正科技有限公司 | Pretreatment method and system for material surface before laser welding |
| CN107121398A (en) * | 2017-04-25 | 2017-09-01 | 苏州德威尔卡光电技术有限公司 | Determination method and device, laser cleaning method and the system of laser cleaning energy |
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| CN107025651A (en) * | 2017-04-25 | 2017-08-08 | 苏州德威尔卡光电技术有限公司 | The determination method and device of laser cleaning energy |
| CN107340302A (en) * | 2017-07-06 | 2017-11-10 | 武汉翔明激光科技有限公司 | A kind of cleaning quality monitoring device and method based on laser cleaner |
| CN107340302B (en) * | 2017-07-06 | 2019-09-03 | 武汉翔明激光科技有限公司 | A kind of cleaning quality monitoring device and method based on laser cleaner |
| CN107610125A (en) * | 2017-10-16 | 2018-01-19 | 云南电网有限责任公司临沧供电局 | A kind of long distance laser derusting monitoring in real time and feedback method, apparatus and system |
| CN108416771A (en) * | 2018-03-07 | 2018-08-17 | 南京工业大学 | Metal material corrosion area detection method based on monocular camera |
| CN110398499A (en) * | 2018-04-23 | 2019-11-01 | 南开大学 | A method of the monitoring laser cleaning process based on high light spectrum image-forming |
| CN111112253A (en) * | 2020-01-16 | 2020-05-08 | 福建省燕京惠泉啤酒股份有限公司 | Fermentation tank cleaning method |
| CN112718710A (en) * | 2020-12-30 | 2021-04-30 | 南开大学 | Method for analyzing copper substrate pollutants and laser cleaning effect thereof based on red, green and blue numerical values |
| CN113083804A (en) * | 2021-04-25 | 2021-07-09 | 中国铁建重工集团股份有限公司 | Laser intelligent derusting method and system and readable medium |
| CN114119535A (en) * | 2021-11-24 | 2022-03-01 | 上海航翼高新技术发展研究院有限公司 | Laser cleaning effect on-line monitoring method based on visual detection |
| CN113962994A (en) * | 2021-12-21 | 2022-01-21 | 武汉智能兴运铁路配件有限公司 | Method for detecting cleanliness of lock pin on three-connecting-rod based on image processing |
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